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Summit on translational bioinformatics最新文献

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Using Personal Health Records for Automated Clinical Trials Recruitment: the ePaIRing Model. 使用个人健康记录进行自动临床试验招募:ep播模型。
Summit on translational bioinformatics Pub Date : 2009-03-01
Adam Wilcox, Karthik Natarajan, Chunhua Weng
{"title":"Using Personal Health Records for Automated Clinical Trials Recruitment: the ePaIRing Model.","authors":"Adam Wilcox,&nbsp;Karthik Natarajan,&nbsp;Chunhua Weng","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>We describe the development of a model describing the use of patient information to improve patient recruitment in clinical trials. This model, named ePaIRing (electronic Participant Identification and Recruitment Model) describes variations in how information flows between stakeholders, and how personal health records can specifically facilitate patient recruitment.</p>","PeriodicalId":89276,"journal":{"name":"Summit on translational bioinformatics","volume":"2009 ","pages":"136-40"},"PeriodicalIF":0.0,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041569/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29693833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Comparative Study of Metabolic Network Topology between a Pathogenic and a Non-Pathogenic Bacterium for Potential Drug Target Identification. 病原细菌与非病原细菌代谢网络拓扑结构的比较研究，用于潜在药物靶标鉴定。
Summit on translational bioinformatics Pub Date : 2009-03-01
Deepak Perumal, Chu Sing Lim, Meena K Sakharkar
{"title":"A Comparative Study of Metabolic Network Topology between a Pathogenic and a Non-Pathogenic Bacterium for Potential Drug Target Identification.","authors":"Deepak Perumal,&nbsp;Chu Sing Lim,&nbsp;Meena K Sakharkar","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Metabolic network provides a unified platform to integrate all the biological information on genes, proteins, metabolites, drugs and drug targets for a comprehensive system level study of the relationship between metabolism and disease. In recent times, drug-target identification by in silico methods has emerged causing a phenomenal achievement in the field of drug discovery. This paper focuses on describing how microbial drug target identification can be carried out using bioinformatic tools. Specifically, it highlights the use of metabolic 'choke point' and 'load point' analyses to understand the local and global properties of metabolic networks in Pseudomonas aeruginosa and allow us to identify potential drug targets. We also list out top 10 choke point enzymes based on the load point values and the number of shortest paths. A non-pathogenic bacterial strain Pseudomonas putida KT2440 and a related pathogenic bacteria P.aeruginosa PA01 was selected for the network anlaysis. A comparative study of the metabolic networks of these two microbes highlights the analogies and differences between their respective pathways. System analysis of metabolic networks will help us in identifying new drug targets which in turn will generate more in-depth understanding of the mechanism of diseases and thus provide better guidance for drug discovery.</p>","PeriodicalId":89276,"journal":{"name":"Summit on translational bioinformatics","volume":"2009 ","pages":"100-4"},"PeriodicalIF":0.0,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041556/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29694505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Automatically classifying sentences in full-text biomedical articles into introduction, methods, results and discussion. 生物医学全文文章中的句子自动分类为引言、方法、结果和讨论。
Summit on translational bioinformatics Pub Date : 2009-03-01
Shashank Agarwal, Hong Yu
{"title":"Automatically classifying sentences in full-text biomedical articles into introduction, methods, results and discussion.","authors":"Shashank Agarwal,&nbsp;Hong Yu","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>BIOMEDICAL TEXTS CAN BE TYPICALLY REPRESENTED BY FOUR RHETORICAL CATEGORIES: introduction, methods, results and discussion (IMRAD). Classifying sentences into these categories can benefit many other text-mining tasks. Although many studies have applied approaches to automatically classify sentences in MEDLINE abstracts into the IMRAD categories, few have explored the classification of sentences that appear in full-text biomedical articles. We explored different approaches to automatically classify a sentence in a full-text biomedical article into the IMRAD categories. Our best system is a support vector machine classifier that achieved 81.30% accuracy, which is significantly higher than baseline systems.</p>","PeriodicalId":89276,"journal":{"name":"Summit on translational bioinformatics","volume":"2009 ","pages":"6-10"},"PeriodicalIF":0.0,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041564/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29694637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Translational systems genomics: ontology and imaging. 翻译系统基因组学:本体和成像。
Summit on translational bioinformatics Pub Date : 2009-03-01
Su-Shing Chen, Yu-Ping Wang
{"title":"Translational systems genomics: ontology and imaging.","authors":"Su-Shing Chen,&nbsp;Yu-Ping Wang","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>In developing an integrated framework for translational bioinformatics, we consider bioimaging in the NIH Roadmap that exploits high-resolution genomic imaging for clinical applications to the diagnosis and treatment of genetic disorders/diseases. On one hand, we develop new image processing techniques, while on the other, we use the fusion of several well known ontological standards - Gene Ontology (GO), Clinical Bioinformatics Ontology (CBO), Foundational Model of Anatomy (FMA) and Microarry Gene Expression Data Ontology (MGED) in this framework. We have discovered that the heterogeneity of the imaging data can be resolved at the different ontological levels of this framework. Moreover, structural genomic information can be readily integrated into the usual textual clinical information bases.</p>","PeriodicalId":89276,"journal":{"name":"Summit on translational bioinformatics","volume":"2009 ","pages":"21-5"},"PeriodicalIF":0.0,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041582/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29694054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Modeling and characterization of disease associated subnetworks in the human interactome using machine learning. 使用机器学习对人类互动组中疾病相关子网络进行建模和表征。
Summit on translational bioinformatics Pub Date : 2009-03-01
Lee T Sam, George Michailidis
{"title":"Modeling and characterization of disease associated subnetworks in the human interactome using machine learning.","authors":"Lee T Sam,&nbsp;George Michailidis","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The availability of large-scale, genome-wide data about the molecular interactome of entire organisms has made possible new types of integrative studies, making use of rapidly accumulating knowledge of gene-disease associations. Previous studies have established the presence of functional biomodules in the molecular interaction network of living organisms, a number of which have been associated with the pathogenesis and progression of human disease. While a number of studies have examined the networks and biomodules associated with disease, the properties that contribute to the particular susceptibility of these subnetworks to disruptions leading to disease phenotypes have not been extensively studied. We take a machine learning approach to the characterization of these disease subnetworks associated with complex and single-gene diseases, taking into account both the biological roles of their constituent genes and topological properties of the networks they form.</p>","PeriodicalId":89276,"journal":{"name":"Summit on translational bioinformatics","volume":"2009 ","pages":"1-28"},"PeriodicalIF":0.0,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041579/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29694630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Pattern discovery in breast cancer specific protein interaction network. 乳腺癌特异性蛋白相互作用网络的模式发现。
Summit on translational bioinformatics Pub Date : 2009-03-01
Xiaogang Wu, Scott H Harrison, Jake Yue Chen
{"title":"Pattern discovery in breast cancer specific protein interaction network.","authors":"Xiaogang Wu,&nbsp;Scott H Harrison,&nbsp;Jake Yue Chen","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>The interest in indentifying novel biomarkers for early stage breast cancer (BRCA) detection has become grown significantly in recent years. From a view of network biology, one of the emerging themes today is to re-characterize a protein's biological functions in its molecular network. Although many methods have been presented, including network-based gene ranking for molecular biomarker discovery, and graph clustering for functional module discovery, it is still hard to find systems-level properties hidden in disease specific molecular networks. We reconstructed BRCA-related protein interaction network by using BRCA-associated genes/proteins as seeds, and expanding them in an integrated protein interaction database. We further developed a computational framework based on Ant Colony Optimization to rank network nodes. The task of ranking nodes is represented as the problem of finding optimal density distributions of \"ant colonies\" on all nodes of the network. Our results revealed some interesting systems-level pattern in BRCA-related protein interaction network.</p>","PeriodicalId":89276,"journal":{"name":"Summit on translational bioinformatics","volume":"2009 ","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041566/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29694636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Using the weighted keyword model to improve information retrieval for answering biomedical questions. 利用加权关键字模型改进生物医学问题的信息检索。
Summit on translational bioinformatics Pub Date : 2009-03-01
Hong Yu, Yong-Gang Cao
{"title":"Using the weighted keyword model to improve information retrieval for answering biomedical questions.","authors":"Hong Yu,&nbsp;Yong-Gang Cao","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Physicians ask many complex questions during the patient encounter. Information retrieval systems that can provide immediate and relevant answers to these questions can be invaluable aids to the practice of evidence-based medicine. In this study, we first automatically identify topic keywords from ad hoc clinical questions with a Condition Random Field model that is trained over thousands of manually annotated clinical questions. We then report on a linear model that assigns query weights based on their automatically identified semantic roles: topic keywords, domain specific terms, and their synonyms. Our evaluation shows that this weighted keyword model improves information retrieval from the Text Retrieval Conference Genomics track data.</p>","PeriodicalId":89276,"journal":{"name":"Summit on translational bioinformatics","volume":"2009 ","pages":"143-7"},"PeriodicalIF":0.0,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041568/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29693834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Multi-criteria decision making approaches for quality control of genome-wide association studies. 全基因组关联研究质量控制的多标准决策方法。
Summit on translational bioinformatics Pub Date : 2009-03-01
Alberto Malovini, Carla Rognoni, Annibale Puca, Riccardo Bellazzi
{"title":"Multi-criteria decision making approaches for quality control of genome-wide association studies.","authors":"Alberto Malovini,&nbsp;Carla Rognoni,&nbsp;Annibale Puca,&nbsp;Riccardo Bellazzi","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Experimental errors in the genotyping phases of a Genome-Wide Association Study (GWAS) can lead to false positive findings and to spurious associations. An appropriate quality control phase could minimize the effects of this kind of errors. Several filtering criteria can be used to perform quality control. Currently, no formal methods have been proposed for taking into account at the same time these criteria and the experimenter's preferences. In this paper we propose two strategies for setting appropriate genotyping rate thresholds for GWAS quality control. These two approaches are based on the Multi-Criteria Decision Making theory. We have applied our method on a real dataset composed by 734 individuals affected by Arterial Hypertension (AH) and 486 nonagenarians without history of AH. The proposed strategies appear to deal with GWAS quality control in a sound way, as they lead to rationalize and make explicit the experimenter's choices thus providing more reproducible results.</p>","PeriodicalId":89276,"journal":{"name":"Summit on translational bioinformatics","volume":"2009 ","pages":"74-8"},"PeriodicalIF":0.0,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041572/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29694500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Towards interoperable reporting standards for omics data: hopes and hurdles. 面向组学数据的互操作报告标准:希望与障碍。
Summit on translational bioinformatics Pub Date : 2009-03-01
Susanna-Assunta Sansone, Philippe Rocca-Serra, Dawn Field, Chris F Taylor, Weida Tong, Marco Brandizi, Eamonn Maguire, Nataliya Sklyar
{"title":"Towards interoperable reporting standards for omics data: hopes and hurdles.","authors":"Susanna-Assunta Sansone,&nbsp;Philippe Rocca-Serra,&nbsp;Dawn Field,&nbsp;Chris F Taylor,&nbsp;Weida Tong,&nbsp;Marco Brandizi,&nbsp;Eamonn Maguire,&nbsp;Nataliya Sklyar","doi":"","DOIUrl":"","url":null,"abstract":"<p><strong>Background: </strong>As the size and complexity of scientific datasets and the corresponding information stores grow, standards for collecting, describing, formatting, submitting and exchanging information are playing an increasingly active role. Several initiatives occupy strategic positions in the international scenario, both within and across domains. However, the job of harmonising reporting standards is still very much a work in progress; both software interoperability and the data integration remain challenging as things stand.</p><p><strong>Results: </strong>The status quo with respect to standardization initiatives is summarized here, with particular emphasis on the motivation for, and the challenges of, ongoing synergistic activities amongst the academic community focused on the creation of truly interoperable standards.</p><p><strong>Conclusions: </strong>Groups generating standards should engage with ongoing cross-domain activities to simplify the integration of heterogeneous data sets to the greatest possible extent.</p>","PeriodicalId":89276,"journal":{"name":"Summit on translational bioinformatics","volume":"2009 ","pages":"112-5"},"PeriodicalIF":0.0,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041584/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29693827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Extraction of Conditional Probabilities of the Relationships Between Drugs, Diseases, and Genes from PubMed Guided by Relationships in PharmGKB. 基于PharmGKB关系的PubMed药物、疾病和基因关系的条件概率提取
Summit on translational bioinformatics Pub Date : 2009-03-01
Martin Theobald, Nigam Shah, Jeff Shrager
{"title":"Extraction of Conditional Probabilities of the Relationships Between Drugs, Diseases, and Genes from PubMed Guided by Relationships in PharmGKB.","authors":"Martin Theobald,&nbsp;Nigam Shah,&nbsp;Jeff Shrager","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Guided by curated associations between genes, treatments (i.e., drugs), and diseases in pharmGKB, we constructed n-way Bayesian networks based on conditional probability tables (cpt's) extracted from co-occurrence statistics over the entire Pubmed corpus, producing a broad-coverage analysis of the relationships between these biological entities. The networks suggest hypotheses regarding drug mechanisms, treatment biomarkers, and/or potential markers of genetic disease. The cpt's enable Trio, an inferential database, to query indirect (inferred) relationships via an SQL-like query language.</p>","PeriodicalId":89276,"journal":{"name":"Summit on translational bioinformatics","volume":"2009 ","pages":"124-8"},"PeriodicalIF":0.0,"publicationDate":"2009-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041559/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"29693829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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